Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44374
標題: A recurrent fuzzy-network-based inverse modeling method for a temperature system control
作者: Juang, C.F.
莊家峰
Chen, J.S.
關鍵字: direct inverse control
fuzzy control
parameter learning
neural
network
water bath temperature control
identification
algorithms
期刊/報告no:: Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Volume 37, Issue 3, Page(s) 410-417.
摘要: Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if-then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified.
URI: http://hdl.handle.net/11455/44374
ISSN: 1094-6977
文章連結: http://dx.doi.org/10.1109/tsmcc.2007.893275
Appears in Collections:電機工程學系所

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